Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorithms. However, for existing semantic segmentation, it has a limitation on the receptive field of high-resolution remote sensing images, which means that it can not show the long-distance scene well during pixel classification, and the image features is compressed during down-sampling, meaning that the detailed information is lost. In order to address these issues, Hybrid Multi-resolution and Transformer semantic extraction Network (HMRT) is proposed in this paper, by which a global receptive field for each pixel can be provided, a small receptive field of convolutional neural networks (CNN) can be overcome, and the ability of scene understanding can be enhanced well. Firstly, we blend the features by branches of different resolutions to keep the high-resolution and multi-resolution during down-sampling and fully retain feature information. Secondly, we introduce the Transformer sequence feature extraction network and use encoding and decoding to realize that each pixel has the global receptive field. The recall, F1, OA and MIoU of HMPR obtain 85.32%, 84.88%, 85.99% and 74.19%, respectively, in the main experiment and reach 91.29%, 90.41%, 91.32% and 84.00%, respectively, in the generalization experiment, which prove that the method proposed is better than existing methods.

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Multi-Resolution Transformer Network for Building and Road Segmentation of Remote Sensing Image

Quantization, which involves bit-width reduction, is considered as one of the most effective approaches to rapidly and energy-efficiently deploy deep convolutional neural networks (DCNNs) on resource-constrained embedded hardware. However, bit-width reduction on the weights and activations of DCNNs seriously degrades accuracy. To solve this problem, in this paper we propose a mixed hardware-friendly quantization (MXQN) method that applies fixed-point quantization and logarithmic quantization for DCNNs without the necessity to retrain and fine-tune the DCNN. Our MXQN algorithm is a multi-staged process where, first, we employ a signal-to-quantization-noise ratio (SQNR) process as the metric to estimate the interplay between the parameter quantization errors of each layer and the overall model prediction accuracy. Then, we utilize a fixed-point quantization process to quantize weights, and depending on the SQNR metric we empirically select either a logarithmic or a fixed-point quantization process to quantize activations. For improved accuracy, we propose an optimized logarithmic quantization scheme that affords a fine-grained step size. We evaluate the performance of MXQN utilizing the VGG16 network on the MNIST, CIFAR-10, CIFAR-100, and the ImageNet datasets, as well as VGG19 and ResNet (ResNet18, ResNet34, ResNet50) networks on the ImageNet, and demonstrate that the MXQN-quantized DCNN despite not being retrained and fine-tuned, it still achieves high accuracy close to the original DCNN.

MXQN:Mixed quantization for reducing bit-width of weights and activations in deep convolutional neural networks

Building and road extraction from remote sensing images is of great significance to urban planning At present, most of building and road extraction models adopt deep learning semantic segmentation method However, the existing semantic segmentation methods did not pay enough attention to the feature information between hidden layers, which led to the neglect of the category of context pixels in pixel classification, resulting in these two problems of large-scale misjudgment of buildings and disconnection of road extraction In order to solve these problem, this paper proposes a Non-Local Feature Search Network (NFSNet) that can improve the segmentation accuracy of remote sensing images of buildings and roads, and to help achieve accurate urban planning By strengthening the exploration of hidden layer feature information, it can effectively reduce the large area misclassification of buildings and road disconnection in the process of segmentation Firstly, a Self-Attention Feature Transfer (SAFT) module is proposed, which searches the importance of hidden layer on channel dimension, it can obtain the correlation between channels Secondly, the Global Feature Refinement (GFR) module is introduced to integrate the features extracted from the backbone network and SAFT module, it enhances the semantic information of the feature map and obtains more detailed segmentation output The comparative experiments demonstrate that the proposed method outperforms state-of-the-art methods, and the model complexity is the lowest

Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image

Dictionary learning has become a research hotspot. How to construct a robust dictionary is a key issue. In face recognition problem, differences in expressions, postures, and lighting conditions are key factors that affect the accuracy. Therefore, images of the same face can be very different in different situations. In real-world scenario, the samples of each face are very limited, which make it hard for the network to generalize well. Therefore, To solve the problem mentioned above, this paper proposes a method to construct a robust dictionary. In the method, virtual samples are generated to appropriately reflect the diversity of the face images, and based on this, two dictionaries are constructed and a corresponding fusion classification scheme is designed. The main advantages of this method are as follows: firstly, the simultaneous use of virtual samples and original samples can better reflect the facial appearance of each morphology, and the dictionaries obtained help to improve the robustness of the dictionary learning algorithm. Secondly, the proposed fusion classification scheme can give full play to the performance of the double dictionary learning algorithm. The results of out experiments show that the proposed algorithm is superior to some existing algorithms.

Dictionary learning and face recognition based on sample expansion

Intelligent Image Semantic Segmentation: A Review Through Deep Learning Techniques for Remote Sensing Image Analysis

It seems that for multiple available images of the same object, the pixel values at the same image position are almost always different, which is especially obvious for the deformable object. This implies that it will be not easy to correctly classify the deformable object. In order to extract salient features of images and improve the performance of image classification, a novel image classification algorithm is proposed in this paper. The algorithm can effectively preserve the large-scale information and global features of the original image, reduce the difference in different images of the same object, and significantly improve the accuracy of image classification. Firstly, the virtual image is generated by the new image representation procedure. Secondly, the image classification algorithm is used to obtain the corresponding classification scores of the original image and the virtual image, respectively. Finally, the ultimate classification score is obtained by a simple and efficient score fusion scheme. A large number of experiments on three widely used image databases show that the proposed algorithm outperforms other state-of-the-art algorithms in classification accuracy. At the same time, the algorithm has the advantages of simple implementation and high computational efficiency.

Improved image representation and sparse representation for image classification

The semantic segmentation of remote sensing (RS) image is a hot research field. With the development of deep learning, the semantic segmentation based on a full convolution neural network greatly improves the segmentation accuracy. The amount of information on the RS image is very large, but the sample size is extremely uneven. Therefore, even the common network can segment RS images to a certain extent, but the segmentation accuracy can still be greatly improved. The common neural network deepens the network to improve the classification accuracy, but it has a lot of loss to the target spatial features and scale features, and the existing common feature fusion methods can only solve some problems. A segmentation network is built to solve the above problems very well. The network employs the InceptionV-4 network as the backbone and improves it. We modify the network structure and introduce the changed Atrous Spatial Pyramid Pooling module to extract the multi-scale features of the target from different training stages. Without losing the depth of the network, using Inception blocks to strengthen the width of the network can obtain more abstract features. At the same time, the backbone network is used for semantic fusion of the context, it can retain more spatial features, then an effective decoder network is designed. Finally, evaluate our model on the ISPRS 2D Semantic Labeling Contest Potsdam and Inria Aerial Image Labeling Dataset. The results show that the network has very superior performance, reaching 89.62% IOU score and 94.49% F1 score on the Potsdam dataset, and the IOU score on the Inria dataset has been greatly improved.

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A New Multi-Channel Deep Convolutional Neural Network for Semantic Segmentation of Remote Sensing Image

In recent years, dictionary learning has received more and more attention in the study of face recognition. However, most dictionary learning algorithms directly use the original training samples to learn the dictionary, ignoring noise existing in the training samples. For example, there are differences between different images of the same subject due to changes in illumination, expression, etc. To address the above problems, this paper proposes the dictionary relearning algorithm (DRLA) based on locality constraint and label embedding, which can effectively reduce the influence of noise on the dictionary learning algorithm. In our proposed dictionary learning algorithm, first, the initial dictionary and coding coefficient matrix are directly obtained from the training samples, and then the original training samples are reconstructed by the product of the initial dictionary and coding coefficient matrix. Finally, the dictionary learning algorithm is reapplied to obtain a new dictionary and coding coefficient matrix, and the newly obtained dictionary and coding coefficient matrix are used for subsequent image classification. The dictionary reconstruction method can partially eliminate noise in the original training samples. Therefore, the proposed algorithm can obtain more robust classification results. The experimental results demonstrate that the proposed algorithm performs better in recognition accuracy than some state-of-the-art algorithms.

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A Dictionary Learning Algorithm Based on Dictionary Reconstruction and Its Application in Face Recognition

The remote sensing (RS) images are widely used in various industries, among which semantic segmentation of RS images is a common research direction. At the same time, because of the complexity of target information and the high similarity of features between the classes, this task is very challenging. In recent years, semantic segmentation algorithms of RS images have emerged in an endless stream, but most of them are improved around the scale features of the target, and the accuracy has great room for improvement. In this case, we propose a semantic segmentation framework for RS images with dynamic perceptual loss. The framework is improved based on the InceptionV-4 network to form a network that includes contextual semantic fusion and dual-channel atrous spatial pyramid pooling (ASPP). The semantic segmentation network is an encoder-decoder structure. In addition, we design a dynamic perceptual loss module and a dynamic loss fusion strategy by further observing the loss changes of the network, so as to better improve the classified details. Finally, experiment on the ISPRS 2D Semantic Labeling Contest Vaihingen Dataset and Massachusetts Building Dataset. Compared with some segmentation networks, our model has excellent performance.

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Wenjie Liu

Papers

Improved image representation and sparse representation for image classification

Dictionary learning and face recognition based on sample expansion

A New Multi-Channel Deep Convolutional Neural Network for Semantic Segmentation of Remote Sensing Image

A Dictionary Learning Algorithm Based on Dictionary Reconstruction and Its Application in Face Recognition

Semantic Segmentation Network of Remote Sensing Images With Dynamic Loss Fusion Strategy